The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W4, 1–6, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W4-1-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W4, 1–6, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W4-1-2017

  10 May 2017

10 May 2017

CONVOLUTIONAL NEURAL NETWORK FOR CAMERA POSE ESTIMATION FROM OBJECT DETECTIONS

E. V. Shalnov1 and A. S. Konushin1,2 E. V. Shalnov and A. S. Konushin
  • 1MSU, Faculty of Computational Mathematics and Cybernetics, Russia, 119991, Moscow, GSP-1, 1-52, Leninskiye Gory, Russia
  • 2HSE, Faculty of Computer Science, Russia, 125319, Moscow, 3, Kochnovsky Proezd, Russia

Keywords: Camera Pose, CNN, Head Detection, Computer Graphics

Abstract. Known scene geometry and camera calibration parameters give important information to video content analysis systems. In this paper, we propose a novel method for camera pose estimation based on people observation in the input video captured by static camera. As opposed to previous techniques, our method can deal with false positive detections and inaccurate localization results. Specifically, the proposed method does not make any assumption about the utilized object detector and takes it as a parameter. Moreover, we do not require a huge labeled dataset of real data and train on the synthetic data only. We apply the proposed technique for camera pose estimation based on head observations. Our experiments show that the algorithm trained on the synthetic dataset generalizes to real data and is robust to false positive detections.